SciELO - Scientific Electronic Library Online

vol.21 issue4Content-based SMS Classification: Statistical Analysis for the Relationship between Number of Features and Classification PerformanceLearning to Answer Questions by Understanding Using Entity-Based Memory Network author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Computación y Sistemas

Print version ISSN 1405-5546


VERMA, Rakesh  and  LEE, Daniel. Extractive Summarization: Limits, Compression, Generalized Model and Heuristics. Comp. y Sist. [online]. 2017, vol.21, n.4, pp.787-798. ISSN 1405-5546.

Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization problem, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and some existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data.

Keywords : Automatic summarization; extractive summarization.

        · text in English     · English ( pdf )